Antenna Season Report Notebook¶

Josh Dillon, Last Revised January 2022

This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.

In [1]:
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
In [2]:
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
In [3]:
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "35"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
In [4]:
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))

Antenna 35 Report

In [5]:
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
In [6]:
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 50 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 48 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
In [7]:
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'

Load relevant info from summary CSVs¶

In [8]:
this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break
In [9]:
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])

Table 1: Per-Night RTP Summary Info For This Atenna¶

This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.

In [10]:
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))

Antenna 35, Node N06:

Out[10]:
JDs A Priori Status Auto Metrics Flags Dead Fraction in Ant Metrics (Jee) Dead Fraction in Ant Metrics (Jnn) Crossed Fraction in Ant Metrics Flag Fraction Before Redcal Flagged By Redcal chi^2 Fraction ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score Average Dead Ant Metric (Jee) Average Dead Ant Metric (Jnn) Average Crossed Ant Metric Median chi^2 Per Antenna (Jee) Median chi^2 Per Antenna (Jnn)
2459865 not_connected 100.00% 0.00% 0.00% 0.00% - - 5.967997 3.516952 2.374233 8.918071 4.742289 4.751411 4.923008 1.409489 0.6320 0.6651 0.3837 nan nan
2459864 not_connected 100.00% 0.00% 0.00% 0.00% - - 5.402308 2.739545 -0.741439 5.458762 2.494369 3.077165 11.484949 1.124569 0.6035 0.6320 0.4362 nan nan
2459863 not_connected 0.00% 0.00% 0.00% 0.00% - - 2.525136 0.673677 -1.267490 -1.451622 -0.659335 -0.647573 3.671752 -0.702633 0.6003 0.6261 0.4216 nan nan
2459862 not_connected 100.00% 0.00% 0.00% 0.00% - - 2.571587 1.166182 -0.340867 7.000387 4.091352 2.846085 1.312650 -0.153737 0.5799 0.6596 0.4399 nan nan
2459861 not_connected 100.00% 0.00% 0.00% 0.00% - - 1.051869 -0.088833 -0.556281 -2.065407 -1.749952 -1.665314 5.952491 -0.237775 0.6200 0.6410 0.4361 nan nan
2459860 not_connected 100.00% 0.00% 0.00% 0.00% - - 1.473432 -0.365746 1.298447 7.449805 3.132243 3.938525 3.940005 -0.659618 0.6271 0.6402 0.4370 nan nan
2459859 not_connected 0.00% 0.00% 0.00% 0.00% - - 0.996454 -0.291767 -0.123074 -2.278446 0.339822 -1.685670 1.178590 -0.054751 0.6263 0.6464 0.4341 nan nan
2459858 not_connected 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 0.852818 -0.157712 -0.411706 -2.449081 -1.694317 -2.013424 5.574541 -0.092626 0.6408 0.6505 0.4436 2.447880 2.601719
2459857 not_connected 0.00% 100.00% 100.00% 0.00% - - 3.492417 0.849837 2.142813 2.380752 1.046394 -0.540921 1.438503 -2.210418 0.0290 0.0292 0.0005 nan nan
2459856 not_connected 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 2.607974 0.652797 -0.958440 5.630443 4.263983 1.030568 1.791026 -0.586575 0.6274 0.6691 0.4338 2.423846 2.653085
2459855 not_connected 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 2.148261 0.502823 0.209023 6.139351 0.086040 0.583900 2.857717 -0.711028 0.6043 0.6752 0.4711 2.719352 2.920611
2459854 not_connected 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 2.226375 0.100135 0.983933 5.855953 0.455754 0.533359 5.025464 0.305822 0.6442 0.7144 0.4587 2.280927 2.360096
2459853 not_connected 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 1.538578 0.286348 3.280390 9.364082 1.348007 2.099451 7.156600 -0.081770 0.6667 0.6555 0.4526 2.817366 3.047641
2459852 not_connected 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 3.025331 1.470292 3.190974 8.691133 1.828017 5.352549 2.880341 5.710893 0.7743 0.8086 0.2760 4.830338 5.483250
2459851 not_connected 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 1.657644 3.130349 0.275280 8.138906 10.050139 11.891126 5.443827 8.403209 0.6687 0.7098 0.3752 2.264655 2.535631
2459850 not_connected 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 1.478256 1.414996 1.179211 6.940693 1.753993 5.309765 8.766453 5.439674 0.6667 0.7239 0.3774 2.581878 2.837892
2459849 not_connected 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 2.329576 -0.030601 1.640870 15.299833 2.364222 1.328892 4.849425 -0.039725 0.6635 0.7193 0.3865 2.999991 3.193454
2459848 not_connected 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 2.616151 0.288918 2.028607 10.778105 7.786274 2.677473 2.767300 -0.391282 0.6349 0.7214 0.4063 2.568509 2.890394
2459847 not_connected 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 2.781507 0.354487 2.897315 9.898209 5.008080 1.934918 3.546192 -0.768376 0.6417 0.6512 0.4570 4.516327 4.617385
2459846 not_connected 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 1.900613 3.045333 0.166639 8.159847 2.408039 5.268061 2.790109 -0.410503 0.8015 0.6537 0.5070 2.766869 1.417140
2459845 not_connected 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 3.461504 0.582340 2.158273 14.908804 4.279218 1.648105 2.112420 -1.265090 0.6621 0.7301 0.4097 0.000000 0.000000
2459844 not_connected 100.00% 100.00% 100.00% 0.00% - - 7.835868 4.028087 55.525076 55.668650 0.968416 0.431693 3.388481 -2.053945 0.0281 0.0284 0.0006 nan nan
2459843 not_connected 100.00% 1.20% 0.66% 0.00% 100.00% 0.00% 3.680015 4.186813 0.417580 15.149002 20.001279 5.526512 1.083987 -2.656929 0.6745 0.7335 0.4300 5.057419 3.518508
2459842 not_connected 0.00% 0.00% 0.00% 0.00% 100.00% 0.00% -1.143693 1.280090 -2.810810 -0.144192 -1.581917 -1.751993 -0.089784 -2.326146 0.7361 0.6111 0.3004 3.700625 3.064528
2459841 not_connected 100.00% 0.00% 0.00% 0.00% - - 40.725691 44.737522 123.135288 121.933350 169.895447 76.180809 11.517915 3.193536 0.7549 0.7343 0.3235 nan nan

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

In [11]:
htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.

In [12]:
for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

Antenna 35: 2459865

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
35 N06 not_connected nn Power 8.918071 5.967997 3.516952 2.374233 8.918071 4.742289 4.751411 4.923008 1.409489

Antenna 35: 2459864

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
35 N06 not_connected ee Temporal Discontinuties 11.484949 2.739545 5.402308 5.458762 -0.741439 3.077165 2.494369 1.124569 11.484949

Antenna 35: 2459863

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
35 N06 not_connected ee Temporal Discontinuties 3.671752 2.525136 0.673677 -1.267490 -1.451622 -0.659335 -0.647573 3.671752 -0.702633

Antenna 35: 2459862

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
35 N06 not_connected nn Power 7.000387 2.571587 1.166182 -0.340867 7.000387 4.091352 2.846085 1.312650 -0.153737

Antenna 35: 2459861

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
35 N06 not_connected ee Temporal Discontinuties 5.952491 -0.088833 1.051869 -2.065407 -0.556281 -1.665314 -1.749952 -0.237775 5.952491

Antenna 35: 2459860

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
35 N06 not_connected nn Power 7.449805 1.473432 -0.365746 1.298447 7.449805 3.132243 3.938525 3.940005 -0.659618

Antenna 35: 2459859

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
35 N06 not_connected ee Temporal Discontinuties 1.178590 0.996454 -0.291767 -0.123074 -2.278446 0.339822 -1.685670 1.178590 -0.054751

Antenna 35: 2459858

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
35 N06 not_connected ee Temporal Discontinuties 5.574541 -0.157712 0.852818 -2.449081 -0.411706 -2.013424 -1.694317 -0.092626 5.574541

Antenna 35: 2459857

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
35 N06 not_connected ee Shape 3.492417 0.849837 3.492417 2.380752 2.142813 -0.540921 1.046394 -2.210418 1.438503

Antenna 35: 2459856

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
35 N06 not_connected nn Power 5.630443 2.607974 0.652797 -0.958440 5.630443 4.263983 1.030568 1.791026 -0.586575

Antenna 35: 2459855

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
35 N06 not_connected nn Power 6.139351 0.502823 2.148261 6.139351 0.209023 0.583900 0.086040 -0.711028 2.857717

Antenna 35: 2459854

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
35 N06 not_connected nn Power 5.855953 0.100135 2.226375 5.855953 0.983933 0.533359 0.455754 0.305822 5.025464

Antenna 35: 2459853

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
35 N06 not_connected nn Power 9.364082 0.286348 1.538578 9.364082 3.280390 2.099451 1.348007 -0.081770 7.156600

Antenna 35: 2459852

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
35 N06 not_connected nn Power 8.691133 3.025331 1.470292 3.190974 8.691133 1.828017 5.352549 2.880341 5.710893

Antenna 35: 2459851

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
35 N06 not_connected nn Temporal Variability 11.891126 1.657644 3.130349 0.275280 8.138906 10.050139 11.891126 5.443827 8.403209

Antenna 35: 2459850

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
35 N06 not_connected ee Temporal Discontinuties 8.766453 1.478256 1.414996 1.179211 6.940693 1.753993 5.309765 8.766453 5.439674

Antenna 35: 2459849

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
35 N06 not_connected nn Power 15.299833 2.329576 -0.030601 1.640870 15.299833 2.364222 1.328892 4.849425 -0.039725

Antenna 35: 2459848

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
35 N06 not_connected nn Power 10.778105 0.288918 2.616151 10.778105 2.028607 2.677473 7.786274 -0.391282 2.767300

Antenna 35: 2459847

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
35 N06 not_connected nn Power 9.898209 0.354487 2.781507 9.898209 2.897315 1.934918 5.008080 -0.768376 3.546192

Antenna 35: 2459846

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
35 N06 not_connected nn Power 8.159847 1.900613 3.045333 0.166639 8.159847 2.408039 5.268061 2.790109 -0.410503

Antenna 35: 2459845

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
35 N06 not_connected nn Power 14.908804 0.582340 3.461504 14.908804 2.158273 1.648105 4.279218 -1.265090 2.112420

Antenna 35: 2459844

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
35 N06 not_connected nn Power 55.668650 7.835868 4.028087 55.525076 55.668650 0.968416 0.431693 3.388481 -2.053945

Antenna 35: 2459843

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
35 N06 not_connected ee Temporal Variability 20.001279 4.186813 3.680015 15.149002 0.417580 5.526512 20.001279 -2.656929 1.083987

Antenna 35: 2459842

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
35 N06 not_connected nn Shape 1.280090 -1.143693 1.280090 -2.810810 -0.144192 -1.581917 -1.751993 -0.089784 -2.326146

Antenna 35: 2459841

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
35 N06 not_connected ee Temporal Variability 169.895447 40.725691 44.737522 123.135288 121.933350 169.895447 76.180809 11.517915 3.193536
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